Electronic Business: Concepts, Methodologies, Tools, and Applications (4-Volumes) P240 potx

2324 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare data integration and knowledge sharing in health- care (Nardon & Moura, 2004). With the recent emergence of EHRs and the need to distribute medical information across organizations, the Semantic Web can allow advances in sharing such information across disparate systems by utilizing ontologies to create a uniform language and by using standards to allow interoperability in trans- mission. The purpose of this article is to provide an overview of how Semantic Web standards and ontologies are utilized in the medical sciences and KH D OW KFD UH¿HOGV:H H [ D P L QHW KHKHD OW KFD UH¿HOG as the inclusion of hospitals, physicians, and others who provide or collaborate in patient healthcare. 7KHPHGLFDOVFLHQFHV¿HOGSURYLGHVPXFKRIWKH research to support the care of patients, and their QHHGOLHVLQEHLQJDEOHWRVKDUHDQG¿QGPHGLFDO research being performed by their colleagues to build upon current work. Interoperability between WKHVHGLIIHUHQWKHDOWKFDUHVWUXFWXUHVLVGLI¿FXOW DQGWKHUHQHHGVWREHDFRPPRQ³GDWDPHGLXP´ to exchange such heterogeneous data (Lee, Patel, Chun, & Geller, 2004). 'HFLVLRQPDNLQJLQWKHPHGLFDO¿HOGLVRIWHQ a shared and distributed process (Artemis, 2005). It has become apparent that the sharing of in- IRUPDWLRQLQWKHPHGLFDOVFLHQFHV¿HOGKDVEHHQ prevented by three main problems: (1) uncommon e x c h a n g e f o r m a t s ; (2) l a c k o f syntactic operability; and (3) lack of semantic interoperability (Decker et al., 2000). Semantic Web applications can be applied to these problems. Berners-Lee, Hendler, DQG /DVVLOD SLRQHHUV LQ WKH ¿HOG RI WKH 6HPDQWLF:HEVXJJHVWWKDW³WKHVHPDQWLFZHE will bring structure to the meaningful content of web pages”. In this article published in 6FLHQWL¿F American, they present a scenario in which some- one can access the Web to retrieve information—to retrieve treatment, prescription, and provider information based on one query. For example, a query regarding a diagnosis of melanoma may provide results which suggest treatments, tests, and providers who accept the insurance plan with which one participates. This is the type of contextually based result that the Semantic Web can provide. The notion of ontologies can be utilized to regulate language, and standards can be used to provide a foundation for representing a n d t r a n s f e r r i n g i n f o r m a t i o n . We w i l l f o c u s o n t h e lack of semantic and syntactic interoperabilities in this article. The semantic interoperable con- cept will be utilized in the context of ontologies, and syntactic interoperabilities are referred to as standards of interoperability. BACKGROUND T h e S e m a n t i c We b i s a n e m e r g i n g a r e a o f r e s e a r c h and technology. Berners-Lee (1989) proposed to the Centre Europeen pour la Recherche Nuclaire (CERN) the concept of the World Wide Web. He has been a pioneer also in the concept of the Semantic Web and has expressed the interest of WKHKHDOWKFDUH¿HOGWRLQWHJUDWHWKHVLORVRIGDWD that exist to enable better healthcare (Updegrove, 2005). He has been involved with the World Wide Web Consortium (W3C) Web site (http://www. w3.org ), which offers a vast array of Semantic Web information in a variety of subject areas, including the medical sciences and healthcare. M i l l e r ( 20 0 4 ) s t a t e s t h a t t h e S e m a n t i c We b s h o u l d SURYLGHFRPPRQGDWDUHSUHVHQWDWLRQWR³IDFLOLWDWH integrating multiple sources to draw new conclu- VLRQV´DQGWR³LQFUHDVHWKHXWLOLW\RILQIRUPDWLRQ E\FRQQHFWLQJLWWRLWVGH¿QLWLRQVDQGFRQWH[W´ Kishore, Sharman, and Ramesh (2004) wrote two articles which provide detailed information about ontologies and information systems. The concept of the Semantic Web is to extend the current World Wide Web such that context and meaning is given to information (Gruetter & Eikemeier, 2004). Instead of information being produced for machines, information will be produced for human consumption (Berners- Lee et al., 2001). There are two main aspects of 2325 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare Semantic Web development: (1) ontologies for consistent terminology and (2) standards for interoperability. Ontologies 2QWRORJLHV KDYH EHHQ GH¿QHG LQ PDQ\ ZD\V through the areas of philosophy, sociology, and computer science. For the Semantic Web con- text, ontology is the vocabulary, terminology, and relationships of a topic area (Gomez-Perez, Fernandez-Lopez, & Corcho, 2004). Ontology gives the meaning and context to information found in Web resources (databases, etc.) for a VSHFL¿FGRPDLQRILQWHUHVWXVLQJ UHODWLRQVKLSV between concepts (Singh, Iyer, & Salam, 2005). According to Pisnalli, Gangemi, Battaglia, and Catenacci (2004), ontologies should have: 1. logical consistency and be expressed in a ³ORJLFDO ODQJXDJH ZLWK DQ H[SOLFLW IRUPDO semantics. 2. semantic coverageVXFKWKDWLWFRYHUV³DOO entities from its domain.” 3. modeling precisionDQGUHSUHVHQW³RQO\WKH intended models for its domain of inter- est.” 4. strong modularityIRUWKHGRPDLQ¶V³FRQ- ceptual space. . .by organizing the domain theories.” 5. scalability so that the language is expressive of intended meanings. The domain of an ontology should include a taxonomy of classes, objects, and their relations, as well as inference rules for associative power (Bern- e r s - L e e e t a l . , 2 0 01) . T h i s s h a r e d u n d e r s t a n d i n g of the concepts and their relationships allows a means to integrate the knowledge between disparate healthcare and medical science systems. Much of the Semantic Web research in the medical sciences DUHDKDVEHHQVSHFL¿FLQHLWKHUJHQHUDWLQJPRUH HI¿FLHQWDQGHIIHFWLYHLQIRUPDWLRQVHDUFKLQJRU to the interoperability of the EHR. Health infor- mation is inherently very tacit and intuitive, and the terminology often implies information based on physical examinations and expressions of the patient. While it uses standardized terminology, WKHGLI¿FXOW\OLHVLQWKHH[SUHVVLRQRIWKLVWDFLW knowledge to others, especially across a network of computers. The two great needs in the medical VFLHQFHVDQGKHDOWKFDUHWKDWFDQEHIXO¿OOHGE\ Semantic Web are to standardize language and to provide a consistent foundation for transferring EHR information (Decker et al., 2000). Standards While ontologies represent the conceptual basis for the information to be transmitted, standards allow for consistent transmission of the data between disparate systems. The data in different clinical information systems silos are in multiple formats, and relevant medical and healthcare knowledge must be accessible in a timely manner. This can be performed through interoperability standards which can enable information integra- WLRQ³SURYLGLQJWUDQVSDUHQF\IRUKHDOWKFDUHUH- lated processes involving all entities within and between hospitals, as well as stakeholders such as pharmacies, insurance providers, healthcare providers, and clinical laboratories” (Singh et al., 2005, p. 30). The main standard for interoperabil- ity in the Semantic Web is Resource Description Framework (RDF), which is recommended by the W3C. RDF is an object-oriented based standard, which provides reusable components for data interchange over the web (Decker, Mitra, et al., 2000). It is unique in that every concept repre- VHQWHGLQ5')KDVDXQLYHUVDOXQLTXHLGHQWL¿HU WKH8QLIRUP5HVRXUFH,GHQWL¿HU>85,@ZKLFK LGHQWL¿HVHYHU\HPDLODGGUHVV:HESDJHDQG other Web elements. This ensures no semantic ambiguity. RDF also enables knowledge repre- sentation through a series of concepts such as class, data type, and values. In order to express 2326 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare representations of ontologies for context, RDF allows for extensions such as the DARPA Agent Markup Language +Ontology Inference Layer (DAML+OIL) standard, which is the basis for the Web Ontology Language (OWL) standard that has recently gained popularity (Nardon & Moura, 2004). SEMANTIC WEB APPLIED STANDARDS AND ONTOLOGIES IN THE MEDICAL SCIENCES AND HEALTHCARE ³7KH VHPDQWLF ZHE LQLWLDWLYH KDVUHVXOWHG LQ D common framework that allows knowledge to be shared and reused across applications” (Health Level 7, 2004) and organizations. An infrastruc- ture of common transmission standards and ter- minology will enable an interconnected network of systems that can deliver patient information. There have been various calls for the decrease of medical errors via utilization of information technology, and the increase of medical informa- tion accessibility and Semantic Web technology has a critical role to play. Besides the delivery of patient information, the Semantic Web can also assist medical sciences research in providing greater accessibility and the sharing of research. In the search for information, the Semantic Web can impart a context and meaning to information VRWKDW TXHULHV DUHPRUHHI¿FLHQWLQSURGXFLQJ results more closely related to the search terms. Table 1 displays only a few of the main stan- dards currently used for interoperability in the 6HPDQWLF:HE7KHDI¿OLDWHGRUJDQL]DWLRQVDUH listed, showing that there are many grassroots efforts involved in generating standards. There are three main organizations that are involved in international standards for EHRs. These include the International Organization for Standardiza- tion (ISO), Committee European Normalization (CEN), and Health Level 7 (HL7)—U.S. based (HL7, 2004). Standards are also important to de- velop on an international basis because countries also report national health status statistics to the world community (Cassidy, 2005). A list of ontologies in the medical domain LV OLVWHG LQ 7DEOH  )RU FODUL¿FDWLRQ D ORJLFDO association to an ontology is that of the ICD-9 (ICD-10 is the new version) coding for diseases. When a patient visits the physician, the physician records a standard ICD-9 code for the diagnosis of the patient and a CPT code for the procedure that was performed on a patient. These are standard- ized codes that are found in manuals for medical coders; and they allow insurance companies and RWKHUPHGLFDODI¿OLDWHVWRXQGHUVWDQGLQIRUPDWLRQ from many different sources. For example, if a patient is seen for a mole, the mole can have many Table 1. Sample standards for interoperability Name Purpose Associated Or g anization Source XML eXtensible Marku p Lan g ua g e ; creation of ta g sDeckeret al , 2000 Nardon , 2004 Gruetter , et al , 2004 Nardon , 2004 Hooda et al 2004 CORBAmed Provides interoperability among health care devices Object Management Grou p McCormack, 2000 HL7 Messa g in g between dis p arate s y stems HL7 www.hl7.or g Guidelines Interchange Format ( GLIF ) specification for structured representation of guidelines InterMed Collaboratory Nardon, 2004 www.glif.org RDF Standardized technology for metadata; for interpreting meanin g W3C Clinical Document Architecture CDA Leading standard for clinical and administrative data exchan g e amon g or g anizations HL7 2327 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare particular qualities. Is it to be removed for cosmetic purposes, or is the mole potentially cancerous? The location of the mole will be important to know, as well, because the treatment may be determined by the location. The difference in the context may determine whether the insurance company will pay for the treatment of the mole. A cancerous melanoma on the nose would have the diagnosis code of 172.3 and a benign neoplasm would be coded as 238.2. If a tissue sample were taken so that the lab could test the mole for cancerous cells, the diagnosis would be 239.9, which is unspeci- ¿HGXQWLOWKHODEUHVXOWVUHWXUQIRUD¿UPGLDJQRVLV The CPT procedure code for the treatment would be applied and would be determined by a number of factors including the location of the mole, amount of WLVVXHH[FLVHGZKHWKHUDPRGL¿HUQHHGVWREHDGGHG WRWKHFRGHLIWKHVHUYLFHVLVFKDUJHGZLWKDQRI¿FH visit, and the type of excision utilized. While we have CPT and ICD-9 as a vocabulary for procedure and diagnosis codes, they function only as a part of ontology’s purpose. An ontology gives context to the patient’s medical history and allows the diagnosis and procedure to be automatically linked, possibly with appropriate medications, lab tests, and x-rays. The next section discusses ways that the Semantic :HEKDVEHHQDSSOLHGLQWKHPHGLFDOVFLHQFHV¿HOG Table 2. Sample ontologies (* is a terminology coding scheme and would be subsumed by an ontol- ogy) Name Pur p ose Associated Or g anization Source Decker et al , 2000 http://www.ontoknowledge.org/o il/oilhome.shtml Ontology Web Language ( OWL ) Aim is to be the Semantic Web standard for ontology representation W3 Consortium Nardon, 2004 Nardon , 2004 htt p ://www.daml.or g / Nardon , 2004 http://cslxinfmtcs.csmc.edu/hl7/ arden/ Hadzic et al , 2005 http://smi- web.stanford.edu/projects/helix/r iboweb.html Hadzic et al , 2005 http://www.geneontology.org/in dex.shtml LinkBase Represents medical terminology by al g orithms in a formal domain ontolo gy L&C Hadzic et al, 2005 GALEN Uses GRAIL language to represent clinical terminolo gy OpenGALEN Gomez-Perez, 2004 ADL Formal language for expressing business rules openEHR www.openEHR.org SNOWMED* Reference terminolo gy SNOMED Int’l Cassid y, 2005 McCormack , 2000 Gilles p ie , 2003 Nardon , 2004 Hadzic , 2005 Gomez-Perez , 2004 ICD-10* Classification of diagnosis codes; is newer version after ICD-9 National Center for Health Statistics Gillespie, 2003 CPT Codes* Classification of procedure codes American Medical Association Gillespie, 2003 UMLS—Unifie d Medical Lan g ua g e Facilitates retrieval and integration of information from multiple sources; can be used as basic ontolo gy for an y medical US National Library of Medicine Gene Ontology To reveal information regarding the role of an organism’s gene products GO Consortium LOINC ( Lo g ical Database for universal names and codes for lab and clinical observations Regenstrief Institute, Inc. Arden Syntax Standard for medical knowledge representation HL7 Riboweb Ontology Facilitate models of ribosomal components and compare research results Helix Group at Stanford Medical Informatics OIL Oil Interchange Language; representation and inference language European Community (IBROW and On-To-Knowledge) DAML Extension of RDF which allows ontologies to be ex p ressed ; formed b y DARPA Marku p DAML Researcher Group 2328 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare SEMANTIC WEB APPLICATIONS IN MEDICAL SCIENCE Table 3 lists only a few of the sample projects being conducted in the medical science and KHDOWKFDUH ¿HOG 3UHYLRXV UHVHDUFKLQ WKLV DUHD KDVGHDOWZLWKWZRPDLQWRSLFVHI¿FLHQWDQG effective searches of medical science informa- tion and (2) the interoperability of EHRs. Our purpose is to provide a comprehensive review of this research to understand the current status of the Semantic Web in healthcare and medical sciences and to determine what future research may be performed. Electronic Health Records EHRs are comprehensive patient medical records which show a continuity of care. They contain a patient’s complete medical history with informa- tion on each visit to a variety of healthcare provid- ers, as well as medical tests and results, prescrip- t i o n s , a n d o t h e r c a r e h i s t o r i e s . (O p p o s e d t o E H R s , Electronic medical records [EMRs] are typically those which reside with one physician.) Figure 1 shows the main stakeholders in the healthcare industry, and thus, the necessity for enabling these partners to communicate. Physician’s, hospitals, Independent Practice Organizations (IPOs), and phar macies interact to exchange patient i nfor ma- tion for medical purposes. The government requires that healthcare organizations report medical data for statisti- cal analysis and so that the overall health of the nation can be assessed. Medical information is DJJUHJDWHGVRWKDWSDWLHQWLGHQWL¿HUVDUHRPLWWHG and reported to the government for public health purposes and to catch contagious outbreaks early as well as to determine current health issues and how they can be addressed. For example, cancer Table 3. Sample medical Semantic Web projects PROJECTS Name Purpose Associated Organization Source Nardon , 2004 http://www.chime.ucl.ac.uk/wor k-areas/ehrs/GEHR/index.htm Brazilian National Health Card Aimed at creating infrastructure for capture of encounter information at the p oint of care Nardon, 2004 Bicer et al. , 2005 http://www.srdc.metu.edu.tr/we bpage/projects/artemis/ Active Semantic Electronic Patient Record Development of populated ontologies in the healthcare (specially cardiology practice) domain; an annotation tool for annotation of patient records, and decision support algorithms that support rule and ontology based checking/validation and evaluation. LSDIS (large Scale Distributed Information Systems and AHC (Athens Heart Center) http://lsdis.cs.uga.edu/projects/a sdoc/ MedISeek Allows users to describe, store, and retrieve medical images; metadata model Carro et al., 2003 Good European Health Record Project To produce a comprehensive multi- media data architecture for EHRs CHIME Artemis Semantic Web Service-based P2P Infrastructure for the Interoperability of Medical Information S y stems Six participating entities from 2329 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare UHJLVWULHV UHSRUWVSHFL¿FDJJUHJDWHGFDQFHU LQ- formation, and healthcare organizations report instances of certain infectious diseases such as WKH$YLDQLQÀXHQ]DELUGÀXIRUWKHZHOIDUHRI the public. The importance of sharing this infor- mation is the improvement of patient safety, ef- ¿FLHQF\VHOIKHDOWKPDQDJHPHQWWKURXJKDFFHVV of medical information), and effective delivery of healthcare (HL7, 2005). Figure 2 shows how two entities may interact to share information (adapted from HL7). Indeed, a commission on systemic interoper- ability has been established through the Medicare Modernization Act of 2003 and recommends SURGXFW FHUWL¿FDWLRQ LQWHURSHUDEOH VWDQGDUGV and standard vocabulary as a way of ensuring that healthcare data is readily accessible (Vijayan, 2005). At a North Carolina Healthcare Informa- tion Communications Alliance, one recurring theme was that of interoperable EHRs. Brailler  WKH ¿UVW 1DWLRQDO +HDOWK ,QIRUPDWLRQ Technology Coordinator in the U.S., spoke about standards harmonization for EHRs. The discus- sion of developing standards for interoperability HPSKDVL]HGWKHQHHGWR³VWLWFKWRJHWKHUGLIIHUHQW efforts” put forth by organizations such as HL7, IEEE, ISO, and SNOMED. Undoubtedly, he UHFRJQL]HGWKDW³VWDQGDUGVDUHDERXWHFRQRPLF p o we r ” a n d t h e y n e e d t o b e a n a l y z e d t o d e t e r m i n e which standards are available for the commercial PDUNHW,QGRLQJVRWKHRI¿FHRI1DWLRQDO+HDOWK Information Technology suggests that there be D FRPSOLDQFH FHUWL¿FDWLRQ IRU (+5 EDVHG RQ criteria such as security, interoperability, and Figure 1. The coordination of the healthcare industry is very diverse in its information needs Electronic Patient Record Insurance Company IPO Hospital Government Pharmacy Physician’s Office Healthcare Industry Coordination Structure 2330 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare clinical standards—basically a seal of approval that if a healthcare organization purchases such a SURGXFWLWZLOOEH³JXDUDQWHHG´WRKDYHVSHFL¿F LQWHURSHUDELOLW\FHUWL¿FDWLRQ%UDLOOHUVWDWHG³LI LW¶VQRWFHUWL¿HGLW¶VQRWDQ(+5´*LYHQWKLVLW has been suggested that the second generation of EHRs is being developed to communicate with structured datasets, middleware, and messaging between systems (Bernstein, Bruun-Rasmussen, Vingtoft, Andersen, & Nohr, 2005). Perhaps the third generation will provide full scale Seman- tic Web capabilities in which interoperability is seamless. Currently, patient information is kept in silos across the aforementioned organizations; the Semantic Web will enable access to these silos through interoperability standards and consistent language. According to a white paper published by HL7 (2004), an organization which has developed HL7 standards for healthcare, improvements in the IROORZLQJ¿YHDUHDVFDQEHPDGHWKURXJK(+5 standards: (1) interoperability, (2) safety/security, TXDOLW\UHOLDELOLW\HI¿FLHQF\HIIHFWLYHQHVV and (5) communication. To improve these areas, the standards proposed by HL7 include both standardized service interface models for interop- erability, but also standardized concept models and terminologies. The current use of the HL7 standard is for the messaging of data to populate other disparate systems. For example, admissions data of a patient is also sent to the billing system. The problem with current messaging systems, such as HL7, is that they duplicate information across systems. Patient demographic information, for example, can be copied from one system to another, and maintenance of such data can create more messaging between systems (usually within an organization). In Denmark, the examination of EHR use and interoperability has also been an issue of interest (Bernstein et al., 2005). The Danish Health IT Strategy project’s goal is to analyze the variety of grassroots models for EHR information modeling and informatics. The National Board of Health is currently analyzing the SNOWMED ontology for use in its EHR. SNOWMED is an ontology WKDWHQFDSVXODWHVFODVVL¿FDWLRQV\VWHPVVXFKDV I C D 9. A s a r e f e r e n c e t e r m i n o l o g y, it i s m u c h m o r e detailed in the medical concepts that it conveys. This level of detailed information allows the data to be used for quality assurance and resource utilization purposes and allows the EHR to relay m o r e i n f o r m a t i o n t h a n I C D 9 c o d i n g f o r d i a g n o s e s . For example, there are around 13,000 ICD9 codes )LJXUH7KHVKDULQJRILQIRUPDWLRQEHWZHHQKHDOWKFDUHHQWLWLHVFDQHQDEOHPRUHHI¿FLHQWDQGHIIHFWLYH quality of care EHR Care Plans Consultations Medical History Pharmacy Ordering Verification Interactions Prescription 2331 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare for diagnoses and SNOWMED contains 365,000 codes (Cassidy, 2005). Similar to the Denmark project, the Artemis project focuses on develop- ing Semantic Web technology such as ontologies as a foundation to interoperability for medical records. Rather than standardizing the actual documents in the EHR, the goal is to standardize the accessibility of the records through wrappers, Web Services Description Language (WSDL) and S i m p le O bj e c t Ac c e s s P r o t o c o l (S OA P) (A r t e m i s , 2005). Bicer et al. (2005) discuss a project with Artemis in which OWL ontologies are used to map information messages from one entity to another. Pa r t n e r s He al t hc a r e u s e s R D F t o en a ble m e d i- cal history from EHRs to be accessible through computer models which select patients for clinical trials (Salamone, 2005). They utilized Semantic Web Rules Language (SWRL) to write decision support rules for this purpose. The advantage in using the Semantic Web approach is that the FRGLQJLVFRQFLVHÀH[LEOHDQGZRUNVZHOOZLWK large databases. As Eric Neumann of the phar- PDFHXWLFDO FRPSDQ\ 6DQR¿$YHQWLV VXJJHVWV ³ZLWKWKH VHPDQWLFZHE\RXSXEOLVKPHDQLQJ not just data” (Salamone, 2005). Information Searching and Sharing ³2QWRORJLHVFDQHQKDQFHWKHIXQFWLRQLQJRIWKH Web in many ways. They can be used in a simple fashion to improve the accuracy of Web searches” %HUQHUV/HHHWDO7KHGLI¿FXOWLHVDQG complexities of searching for medical informa- tion are discussed by Pisnalli et al. (2004) in their research on medical polysemy. Because polysemy (a word having more than one meaning) can be FULWLFDOWR¿QGLQJFRUUHFWPHGLFDOLQIRUPDWLRQ the application of ontologies can be of value in information searching. For example, the ontology of the term LQÀDPPDWLRQ can vary depending on the context of its use. As Pisnalli et al. state, in- ÀDPPDWLRQFDQLQFOXGHWKHVL]HVKDSHHYROXWLRQ severity, and source. When one searches for the WHUPLQÀDPPDWLRQPDQ\UHVXOWVPD\EHSURYLGHG EXWWLPHLVUHTXLUHGWRVRUWWKURXJKWKH³KLWV´ for relevance. The ON-9 ontology is utilized by 3LVQDOOLHWDOWRPDSFRQWH[WVIRUWKHWHUPLQÀDP- mation. As Nardon and Moura (2004) emphasize, the relationships among medical terminology is also essential to representation of the information LQDORJLFDOIRUPDW$OORZLQJIRUVSHFL¿FFRQWH[W to be interpreted through ontologies will enable PRUHHI¿FLHQWDQGHIIHFWLYHVHDUFKLQJ8VXDOO\ this involves the creation of metadata to identify the relevant data elements and their relationships (Buttler et al., 2002). Medical vocabularies used to represent data LQFOXGHWKH 8QL¿HG0HGLFDO/DQJXDJH 6\VWHP (UMLS) from the U.S. National Library of Medi- cine and Arden Syntax. UMLS is perhaps the most frequently used ontology in the healthcare and PHGLFDOVFLHQFHV¿HOG7KHSXUSRVHLVWRDLGLQ integrating information from multiple biomedical LQIRUPDWLRQVRXUFHVDQGHQDEOLQJHI¿FLHQWDQGHI- IHFWLYHUHWULHYDO,WGH¿QHVUHODWLRQVKLSVEHWZHHQ vocabularies and includes a categorization of concepts as well as the relationships among them. For example, the National Health Card System in Brazil contains an extensive knowledge base of 8 million patients in which complex queries can be run (Nardon & Moura, 2004). Through ontologies and UMLS, mapping of business rules can be applied to medical transactions to infer information and achieve semantic interoperabil- ity. For example, if a patient can undergo only a certain procedure once within a 30-day time period, a transaction for a patient setting up an appointment for that procedure can be mapped to business rules to infer that the same person can- not schedule the same procedure within that time period. UMLS would determine the ontology for the appointment and procedures and ensure that WKHSDWLHQWLVLQGHHGWKHVDPHDQG5')GH¿QHV the business rules for sharing the information (Nardon & Moura, 2004). When querying multiple medical data sources for research purposes, there are many medical 2332 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare science repositories in which data may not be in machine-processable format and stored in non- standard ways. Most of the interfaces to search and retrieve medical sciences research require human interaction. Data extraction of such large data sources can be very complex and often the data is reused by researchers such as those in Genomics (Buttler et al., 2002). Large databases FRQWDLQLQJELRLQIRUPDWLFVUHVHDUFKFDQEHXQL¿HG through ontologies such as Riboweb, Generic Human Disease Ontology, Gene Ontology (GO), TAMBIS, and LinkBase. These allow a standard vocabulary to exist over disparate ribosomal, disease, gene product, nucleic acid, and protein resources. As an example, the Generic Human Disease Ontology, currently being developed with information from the Mayo Clinic, allows a physician to search by symptom to determine the disease or for type of appropriate treatment, and researchers can search for possible causes of a disorder (Hadzic & Chang, 2005). MedISeek is an interesting example of us- ing semantic vocabularies to search for medical visual information, such as x-rays and other images (Carro et al., 2003). Biomedical Imag- ing Research Network (BIRN), a project of the National Institute of Health, examines human neurological disorders and their association with D Q L P D O PRGHOV$ VLJQ L ¿FDQWDVSHFWRI W KHLUZRU N  is through brain imaging. Their goal is to make this information available to others through the Semantic Web via graphical search tools; standard LGHQWL¿HUVWKURXJKRQWRORJLHVDQGFURVVUHIHU- encing of imaging (Halle & Kikinis, 2004). The Semantic Web will enable BIRN, MedISeek, and other healthcare and medical science projects to ¿OWHURXWOHVVDSSURSULDWHGDWDE\VHDUFKLQJIRUD context to the information. RDF is being utilized with MedISeek and BIRN to allow interoperability between metadata patterns. CONCLUSION AND FUTURE TRENDS Sharing of EHR information allows for improved quality of care for patients. Sharing medical science knowledge allows scientists to gather information and avoid redundant experiments. Searching for medical science information on the 6HPDQWLF:HEZLOOEHPDGHPRUHHI¿FLHQWDQG effective by the use of common ontologies and VWDQGDUGVIRUWUDQVPLVVLRQV³7UXVWHGGDWDEDVHV exist, but their schemas are often poorly or not documented for outsiders, and explicit agreement about their contents is therefore rare.” The oppor- tunity to share such large amounts of information through the Semantic Web suggests that knowl- edge management can exist on a comprehensive l e v el w i t h o n t o l o g y a s a u n i f y i n g r e s o u r c e ( H a d z i c & Chang, 2005). While there has been some research in the area of medical sciences information searching on the Semantic Web, there have been few stud- ies on how to better enable healthcare consumers to search for medical information on the Web. Lay terminology of consumers often increases the number of results returned when searching for medical information on the Web. Polysemy creates a multitude of results within which the consumer must further search. The goal should be to use Semantic Web technology to minimize the semantic distance between a search term and its polysemy of translations (Lorence & Spinks, 2004). The future of the Semantic Web will involve important developments in the emergence of e- healthcare through the use of intelligent agents. Singh et al. (2005) suggest that emerging Se- mantic Web-based technologies offer means to DOORZVHDPOHVVDQGWUDQVSDUHQWÀRZRIVHPDQWL- cally enriched information through ontologies, knowledge representation, and intelligent agents. Intelligent agents can enrich the information by 2333 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare interpretation on behalf of the user to perform an automated function. The example given at the beginning of this article in which someone queries for melanoma information and receives information regarding treatments, tests, and providers in that person’s location which accept his insurance, shows how intelligent agents can be utilized to search the Semantic Web. Agents can also be utilized to verify the source of the information. When sharing of information occurs across the Web and is pulled automatically by agents, the source of the information needs to be YHUL¿HG7KLVLVHVSHFLDOO\WUXHLQKHDOWKFDUHZLWK Health Insurance Portability and Accountability Act (HIPAA) 1996 regulations. If the foundation of ontology and interoperable standards exists, intelligent agents will be able to search the Web for information within the context desired. Legal issues associated with the dispersion RIKHDOWKFDUHLQIRUPDWLRQQHHGWREHLGHQWL¿HG With HIPAA (1996)), healthcare organizations DUHUHTXLUHGWRNHHSSDWLHQWSHUVRQDOO\LGHQWL¿- able information secure and private. This means encryption, access control, audit trails, and data integrity must be insured in the transmission process (Jagannathan, 2001). Who has rights to WKHGDWDDQGZKR³RZQVWKHGDWD´SDUWLFXODUO\LQ EHRs? Similarly, there is an issue of trust involved with sharing medical science and healthcare data, and this is an area ripe for further research. How c a n a u t h e n t i c a t i o n b e p r o v i d e d s o t h a t o t h e r s k n o w the source of data is trusted and how can it be ensured that the data will be edited by a trusted entity? The area of e-commerce can be a founda- tion for future research in trust, as well. Semantic Web technology can function as a foundation for the sharing and searching of in- formation for the healthcare and medical sciences ¿HOGV%HFDXVHRIWKHLQWXLWLYHQDWXUHRISDWLHQW care, the Semantic Web will enable context and meaning to be applied to medical information, as well as the conveyance of relationships between data. With the generation of standards for trans- mission of data between disparate systems, the quality of healthcare through better research and the sharing of information between healthcare providers will be a critical step in the evolution of patient care. This will enable the third generation of EHRs to be seamlessly interoperable for more HI¿FLHQWDQGHIIHFWLYHSDWLHQWFDUH7KHVHLQQR- vations can lead to improved work satisfaction, patient satisfaction, and patient care (Eysenbach, 2003). REFERENCES Artemis. (2005). Retrieved November 2005, from www.srdc.metu.edu.tr/webpage/projects/artemis/ home.html Berners-Lee, T. (1989). Proposal of Semantic Web to CERN. Retrieved October, 2005 from http://www.w3.org/History/1989/proposal.html Berners-Lee, T., Hendler, J., & Lassila, O. (2001, May 17). The Semantic Web. 7KH 6FLHQWL¿F American. Retrieved May 2005, from www.sciam. com Bernstein, K., Bruun-Rasmussen, M., Vingtoft, S., Andersen, S. K., & Nohr, C. (2005). Model- ling and implementing electronic health records in Denmark. International Journal of Medical Informatics, 74, 213-220. Bicer, V., Laleci, G., Dogac, A., & Kabak, Y. (2005, September). Artemis message exchange frame- work: Semantic interoperability of exchanged messages in the healthcare domain.SIGMOD Record, 34(3), 71-76. Brailler, D. (2005). Keynote address. NCHICA 11 th Annual Conference, Greensboro, NC. Retrieved November 2005, from www.nchica.org . Web Standards and Ontologies in the Medical Sciences and Healthcare data integration and knowledge sharing in health- care (Nardon & Moura, 2004). With the recent emergence of EHRs and the. of 2325 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare Semantic Web development: (1) ontologies for consistent terminology and (2) standards for interoperability. Ontologies 2QWRORJLHV. 2332 Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare science repositories in which data may not be in machine-processable format and stored in non- standard ways. Most